تغییرات جستجو در رفتار خرید مشتری برای توصیه های مشترک
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|27446||2005||11 صفحه PDF||سفارش دهید||6474 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 28, Issue 2, February 2005, Pages 359–369
The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this study, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques, with better performance, especially with regard to heavy users.
Recommender systems have been a recent focus of researchers and practitioners. Many companies hope that the use of recommender systems may be a means of surviving in a competitive environment. Recommender systems are particularly suited to retail business, as compared to other types of business, since retail markets are distinguished by several characteristics, such as repeated buying over a particular time horizon, large numbers of customers, and a wealth of information detailing past customer purchases (Schmittlein & Peterson, 1994). In general, retail companies operate purchase databases in a longitudinal way, such that all transactions are stored in chronological order. A record-of-transaction database typically contains the transaction date for and the products bought in the course of, a given transaction. Usually, each record also contains a customer ID, particularly when the purchase was made using a credit card or a frequent-buyer card. Therefore, the purchasing sequence of a customer in the database who has made repeat purchases can easily be determined. This purchase sequence provides a description of the changes in a customer's preferences over time. However, in our domain of knowledge, there has been little study of the question of whether recommendations based on purchase sequences may be more accurate than existing recommender system predictions, based on non-sequential patterns. In this study, for the purpose of enhancing the quality of recommendations, we propose a new methodology that considers the way in which a customer's purchase sequence evolves over time.
نتیجه گیری انگلیسی
The preferences of customers change over time. In this study, we described a model-based approach for mining the changes in customer buying behavior over time and discussed solutions to several problems: data preprocessing, behavior locus extraction, and recommendation formulation based on extracted loci. Using the derived recommendation list, companies may be able to perform effective one-to-one marketing campaigns by providing individual target customers with personalized product recommendations. The research presented in this paper makes a contribution to the related recommender systems literature. We took into consideration changes in customer preferences to improve the accuracy of the recommendations made. In particular, we determined that the proposed methodology is more suitable for heavy users. Some possible extensions to this work are as follows. From the results of this study, we know which products target customers are likely to buy, but we have not yet explored the times at which these purchases are likely to occur. Further research analyzing customers' past purchasing patterns should likewise enable prediction of the most appropriate times for recommendations to be given. In addition, since the accuracy of all model-based approaches deteriorates as time passes, the model must be dynamically updated to reflect the users' evolving interests. The way in which the predictive capabilities of the model decrease as time passes should be investigated, with the goal of creating a repair plan. Furthermore, one interesting research extension would be the setting up of a real marketing campaign, in which customers would be targeted using our methodology, which could then be evaluated with regard to its performance.